function [result]=Qiang_clustering(dist_matrix)

g{1}=[1:100];
g{2}=[101:200];
g{3}=[201:300];
g{4}=[301:400];
g{5}=[401:500];
g{6}=[501:600];

% repeat the experiment 10 times, then output the average result.
for j=1:10
group=K_Medoids(dist_matrix,length(g));
metric(j) = clustering_metric(g,group);
end

metric
disp 'vraiance'
std(metric)

disp 'mean value'
result = mean(metric)

